LiDAR Validation of a Video-Derived Beachface Topography ... · from 0.5 to 2 m [13,49,50]. In...

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Article

LiDAR Validation of a Video-Derived BeachfaceTopography on a Tidal Flat

David Didier 1 Pascal Bernatchez 1 Emmanuel Augereau 2 Charles Caulet 2Dany Dumont 3 ID Eliott Bismuth 1 Louis Cormier 1 France Flocrsquoh 2 and Christophe Delacourt 2

1 Queacutebec-Oceacutean Centre drsquoeacutetudes nordiques Chaire de Recherche en Geacuteoscience CocirctiegravereUniversiteacute du Queacutebec agrave Rimouski Rimouski QC G5L 3A1 Canada Pascal_Bernatchezuqarca (PB)Eliott_Bismuthuqarca (EB) Louis_Cormieruqarca (LC)

2 Laboratoire Geacuteosciences Oceacutean UMR 6538 Institut Universitaire Europeacuteen de la merUniversiteacute de Bretagne Occidentale Brest 29280 Plouzaneacute Franceemmanuelaugereauuniv-brestfr (EA) charlescauletuniv-brestfr (CC)franceflochuniv-brestfr (FF) christophedelacourtuniv-brestfr (CD)

3 Institut des Sciences de la mer de Rimouski Universiteacute du Queacutebec agrave Rimouski Queacutebec-OceacuteanPhysique des OceacuteansmdashLaboratoire de Rimouski Rimouski QC G5L 3A1 Canada Dany_Dumontuqarca

Correspondence David_Didieruqarca Tel +1-418-723-1986 (ext 1364)

Received 26 June 2017 Accepted 7 August 2017 Published 11 August 2017

Abstract Increasingly used shore-based video stations enable a high spatiotemporal frequencyanalysis of shoreline migration Shoreline detection techniques combined with hydrodynamicconditions enable the creation of digital elevation models (DEMs) However shoreline elevationsare often estimated based on nearshore process empirical equations leading to uncertainties invideo-based topography To achieve high DEM correspondence between both techniques we assessedvideo-derived DEMs against LiDAR surveys during low energy conditions A newly installedvideo system on a tidal flat in the St Lawrence Estuary Atlantic Canada served as a test caseShorelines were automatically detected from time-averaged (TIMEX) images using color ratios inlow energy conditions synchronously with mobile terrestrial LiDAR during two different surveysHydrodynamic (waves and tides) data were recorded in-situ and established two different cases ofwater elevation models as a basis for shoreline elevations DEMs were created and tested againstLiDAR Statistical analysis of shoreline elevations and migrations were made and morphologicalvariability was assessed between both surveys Results indicate that the best shoreline elevationmodel includes both the significant wave height and the mean water level Low energy conditionsand in-situ hydrodynamic measurements made it possible to produce video-derived DEMs virtuallyas accurate as a LiDAR product and therefore make an effective tool for coastal managers

Keywords video monitoring shoreline detection beach morphology mobile terrestrial LiDARerosion Atlantic Canada

1 Introduction

Beaches evolve through time and space influenced by morphogenetic processes occurring inmultiple dimensions in the surf and swash zones [1] Following wave breaking different short-termdynamic processes occur on a beach mostly wave-induced highlow-frequency swash motions [2]groundwaterbed interactions [34] and scouring [5] The beach morphology usually tends towardan equilibrium slope resulting from an onshoreoffshore sediment transport induced by waves andcurrents [6ndash8] Changes also occur on longer time scales where beaches are affected by strong seasonalcycles For example in cold regions the presence of sea ice can protect (ie consolidates beachsediments) or erode (ie scouring at the icefootrsquos toe transport by drift ice etc) the beach profile [9]

Remote Sens 2017 9 826 doi103390rs9080826 wwwmdpicomjournalremotesensing

Remote Sens 2017 9 826 2 of 22

Multi-year analyses of the shoreline position can show variabilities on a kilometer scale on sandycoasts [1011] while geologically constrained and protected systems like platform-beaches [1213] andpocket-beaches [14] can show little changes Capturing relevant morphogenic processes across differentscales is not straightforward namely because of the low temporal resolution of most monitoringstrategies (large time interval between field campaigns) [10] that filter short-term events Alongwith climate change and sea-level rise beach morphodynamics can therefore be confusing andmisunderstood by coastal managers and engineers in need of operational tools in order to dealwith constantly evolving beaches [15] On erosion hotspots [16] it is imperative to better understandthe multi-scale variability of the beach morphology and slope which are essential for flood riskassessment and to design protective structures in suitable adaptation strategies

Out of many technological tools for beach morphology monitoring such as total stations [17]DGPS surveys [18ndash20] radar [21] UAV [22ndash24] or LiDAR [25ndash27] perhaps the shore-based videoimagery is the most versatile Such systems can be installed virtually everywhere for continuousrecording over a long period of time [28] and are not directly vulnerable to extreme events as opposedto in-situ instruments [29] In terms of spatiotemporal resolutions video cameras offer other greatperspectives enabling both short-term and long-term monitoring capabilities Video imagery is clearlyeffective for wave runup measurements including swash and setup [30ndash33] It then can be appliedto calibrate empirical formulae in low and high energy situations through time-stack analysis [3435]Applied on video imagery shoreline detection techniques based on time-exposure images can alsoacquire high-frequency topographical information when combined with hydrodynamic forcing [36ndash38]With a relatively low cost [39] one can obtain digital elevation models (DEMs) with a few centimetresaccuracy [37] However such systems and subsequent image analysis needs to be adapted to localconditions since relationships between image optical characteristics and environmentalgeometricalconditions on beaches are not always understood [40] especially in cold environments where coastalsea ice interferes with sediment dynamics [41]

A video-based shoreline evolution monitoring typically integrates two parameters the shorelinedetection and the water elevation model [42] Swash and wave breaking on reflective beaches typicallyenhance beachwater pixel gradient intensities seen as foamy bright bands facilitating its detectionusing grey-scale images [4344] However detecting shorelines on dissipative beaches with large surfzones is not as straightforward since the cross-shore pixel intensitiesvariance due to waves andfoam motions [38] is scattered along a larger area Another problem arises in enclosed areas withless energetic conditions such as estuaries [45] or on the landward side of sandbars [37] To avoidthese problems some authors have proposed shoreline detection techniques based on full colorcharacteristics of the imagery especially using ARGUS stations [4246] as opposed to traditionalgrey-scale image analysis developed earlier [43] Almar et al [37] also detected shorelines basedon color analysis but added a correction due to alongshore swash shapes at different tidal levels toestimate the intertidal topography at a double-barred and energetic meso-tidal beach in BiscarroseFrance The resulting morphology performed well with GPS validation runs for low energeticconditions (significant wave height Hs lt 1 m) Detected planimetric shorelines can be compared toGPS runs on the beach at the time of the detection with horizontal uncertainties generally varyingbetween ~1 and 6 m [424547]

The water elevation associated with a detected shoreline commonly includes the still waterlevel (astronomical tide plus the barometric surge) and wave-induced runup (including setup andswash) [10374748] The former component the offshore static water level is mostly known within-situ gauge prior wave breaking The latter is more complicated to obtain since foreshore dynamicsare most notably deduced with empirical formulations Aarninkhof et al [42] computed the setupand swash oscillation elevations and concluded that their technique detected a shoreline at the upperend of the swash runup Apart from uncertainties in planimetric shoreline detections and inherentto RTK-GPS validations and ground control points these techniques imply uncertainties associatedwith the empirical formulations used Such equations are limited to the input conditions only and


common vertical errors and deviations between theoretical results and observed values can rangefrom 05 to 2 m [134950] In estuaries with mudflats Morris et al [45] used RTK-DGPS to validatevideo-derived shoreline elevations and obtained vertical errors within 5 cm On more open coasts withmoderate to high energy conditions Vousdoukas et al [47] Uunk et al [36] and Aarninkhof et al [42]also found low elevation error values using field DGPS validations respectively of 26 cm 28 cmand 15 cm Subsequent associations of detected shorelines with empirical formulations seems to showgood results in most cases but local validations appear to be necessary both for detected shorelinesand the associated water levels In low-energy environments such as estuaries and sandflats theabsence of significantly contrasting swash on the beach motivated this study

Beaches along the St Lawrence maritime Estuary (Atlantic Canada) typically show suchcharacteristics Furthermore in such cold environments the progressive disappearance of coastal seaice during winter due to climate change leads to an enhancement of coastal erosion [51] In order toanalyze these multi-scale dynamics efficient methodology to retrieve shoreline evolution is neededThis paper aims to create digital elevations models based on image-detected shorelines and to validatethe video-derived topography on a low-energy meso-tidal beachface with a two-kilometer-widesandflat The shoreline elevations are obtained with in-situ wave and tidal gauges landward of thesurf zone Resulting digital elevation models are compared to simultaneous mobile LiDAR acquisitionresults The LiDAR-interpolated topography acts as a validation tool for the video-based topographyFinally the shoreline displacement rates during the autumn of 2016 are calculated from both videoand LiDAR-based topography in order to compare both methods

2 Materials and Methods

21 Study Area

The Manicouagan Peninsula is located on the north shore of the St Lawrence maritime Estuary(Figure 1) This imposing delta was formed in the Goldthwait Sea during the last deglaciation Duringthe Laurentian transgression which ended around 44ndash42 ka years BP an important erosion platformwas carved in the fine prodeltaic sediments forming today steep beachfaces over a sandy flat as wideas 2 km in the cross-shore direction [52]

Despite previous Quaternary [52ndash54] coastal erosion [51] and sea ice [5556] studies in thearea little is known on the morphodynamics of the Manicouagan beaches Coastal citizensare facing constant erosion and one of the hotspots is located on the beach of Pointe-Lebel(Figure 1c) Shrinking winter sea ice and its predicted disappearance in the second half of the21st century [5556] will potentially increase erosion problems during the cold season by enablingstorm waves to reach the beach Deposited beach materials in Pointe-Lebel consist mainly of coarseand medium sands coming from sedimentary coastal cliffs Beach erosion reached around minus12 m andminus29 myear (1931ndash2001) [51]

Tides around the Manicouagan Peninsula are semi-diurnal and meso-tidal ranging between3 and 43 m and reaching up to 49 m during extreme spring tides [57] with currents reaching3 ms [58] All tidal levels will be subsequently referred to their local geodetical datum (CGVD28)according to a difference of minus181 m with the chart datum (mean sea levels (MSL) = 0 m chart datum(CD) = minus181 m Higher high water large tide (HHWLT) = 289 m Lower low water large tide(LLWLT) = minus119 m) The Pointe-Lebel beach studied in this paper is characterized by a low-tideterracenon-barred dissipative shape (considering the Masselink and Short [59] and Scott et al [60]classifications) It is bounded by two small creeks forming deltas on the lower foreshore (see Figure 1c)The main longshore drift is oriented toward northeast [52] The wide intertidal sandflat is covered insome places with sandy accumulation banks of low amplitude At high tide swash occurs on a morereflective beachface with an average width of 20 m The beachface is delimited on its upper part by anerosion scarp and on its lower limit by a hinge line This break of slope marks the beginning of thelower foreshore which stretches over more than 2 km at low tide

Remote Sens 2017 9 826 4 of 22Remote Sens 2017 9 826 4 of 22

Figure 1 Location of the Manicouagan Peninsula (ab) and of the monitoring station at Pointe-Lebel (c) Two video cameras are installed with 2 instruments (G1 nearshore pressure sensor logger G2 offshore acoustic wave and current meter (AWAC) for in-situ hydrodynamic acquisition (black circles) (bc))

The offshore wave climate is mainly generated from dominant westerlies and strong easterly winds [61] Beaches of the Manicouagan Peninsula can be exposed to strong ENE winds blowing on a 350 km long fetch [53] Being oriented ESE this is especially the case of the Pointe-Lebel beach where the video camera system is located Ruest et al [56] simulated wave heights with winds from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) re-analysis in the Estuary Simulated extreme waves (99th percentile Hs) are between 1 and 2 m while the 50-year return Hs is higher (between 25 and 4 m) for the whole Estuary As per the authors wave heights in the Gulf of St Lawrence are going to increase by 5ndash10 by 2100 with extensive ice-free periods during winter

22 Wave and Tidal Data

Waves were measured with an array of two in-situ gauges between 1 July 2016 and 30 December 2016 (Figure 2) The first sensor (RBR Virtuoso) is located at the toe of the beachface close to the break in the foreshore slope (G1 z = 020 m) landward of the tidal flat This nearshore sensor acquired continuous total pressures at a 4 Hz frequency and was subsequently processed through a spectral analysis over a length of 1024 s (1706 min) Offshore waves were acquired at 2 Hz with an Acoustic Wave and Current meter (AWAC) deployed at 14 m depth (G2) (Figure 2b) southeast of

Figure 1 Location of the Manicouagan Peninsula (ab) and of the monitoring station at Pointe-Lebel (c)Two video cameras are installed with 2 instruments (G1 nearshore pressure sensor logger G2 offshoreacoustic wave and current meter (AWAC) for in-situ hydrodynamic acquisition (black circles) (bc))

The offshore wave climate is mainly generated from dominant westerlies and strong easterlywinds [61] Beaches of the Manicouagan Peninsula can be exposed to strong ENE winds blowingon a 350 km long fetch [53] Being oriented ESE this is especially the case of the Pointe-Lebel beachwhere the video camera system is located Ruest et al [56] simulated wave heights with winds fromthe Modern-Era Retrospective Analysis for Research and Applications (MERRA) re-analysis in theEstuary Simulated extreme waves (99th percentile Hs) are between 1 and 2 m while the 50-yearreturn Hs is higher (between 25 and 4 m) for the whole Estuary As per the authors wave heightsin the Gulf of St Lawrence are going to increase by 5ndash10 by 2100 with extensive ice-free periodsduring winter


Waves were measured with an array of two in-situ gauges between 1 July 2016 and 30 December2016 (Figure 2) The first sensor (RBR Virtuoso) is located at the toe of the beachface close to the breakin the foreshore slope (G1 z = 020 m) landward of the tidal flat This nearshore sensor acquiredcontinuous total pressures at a 4 Hz frequency and was subsequently processed through a spectral


analysis over a length of 1024 s (1706 min) Offshore waves were acquired at 2 Hz with an AcousticWave and Current meter (AWAC) deployed at 14 m depth (G2) (Figure 2b) southeast of Pointe-LebelThe cross-shore distance of both instruments from the HHWLT line on the beach is respectively23 m and 45 km offshore Calculated spectral wave parameters are the significant wave height (Hs)mean periods (Tm02) and offshore mean wave directions θ at G2 Atmospheric pressure was obtainedfrom a barometric pressure gauge (HOBO data logger) every 10 min and interpolated to wave samplingrates Mean water level (η) was calculated from mean pressure on both instruments


Pointe-Lebel The cross-shore distance of both instruments from the HHWLT line on the beach is respectively 23 m and 45 km offshore Calculated spectral wave parameters are the significant wave height (Hs) mean periods (Tm02) and offshore mean wave directions θ at G2 Atmospheric pressure was obtained from a barometric pressure gauge (HOBO data logger) every 10 min and interpolated to wave sampling rates Mean water level (η) was calculated from mean pressure on both instruments

Figure 2 Cross-shore profile (extracted from a shoal survey of 2015 obtain from the Canadian Hydrographic Service 2015) of Pointe-Lebel beach and location of the in-situ instruments (G1 and G2) (a) A focus on the beachface is shown in (b) from the MTLS (Mobile Terrestrial Lidar System) survey of 3 August 2016 Elevations associated to higher high water large tides (HHWLT) mean sea levels (MSL) and lower low water large tides (LLWLT) are shown by horizontal dashed lines

Mean water level fluctuations recorded offshore during the entire joint deployment period oscillated between minus242 and 317 m The sensor G1 recorded a maximum Hs of 098 m on 16 November 2016 During the LiDAR surveys of 3 August and 18 November (vertical lines on Figure 3) the conditions were calm and the maximum mean water levels at G1 reached 148 m and 225 m at high tide respectively Significant wave heights did not exceed 014 m during the surveys

Figure 2 Cross-shore profile (extracted from a shoal survey of 2015 obtain from the CanadianHydrographic Service 2015) of Pointe-Lebel beach and location of the in-situ instruments (G1 and G2)(a) A focus on the beachface is shown in (b) from the MTLS (Mobile Terrestrial Lidar System) surveyof 3 August 2016 Elevations associated to higher high water large tides (HHWLT) mean sea levels(MSL) and lower low water large tides (LLWLT) are shown by horizontal dashed lines

Mean water level fluctuations recorded offshore during the entire joint deployment periodoscillated between minus242 and 317 m The sensor G1 recorded a maximum Hs of 098 m on16 November 2016 During the LiDAR surveys of 3 August and 18 November (vertical lines onFigure 3) the conditions were calm and the maximum mean water levels at G1 reached 148 m and225 m at high tide respectively Significant wave heights did not exceed 014 m during the surveys


Figure 3 Mean water level (with daily tidal cycles) (m) (a) significant wave height (m) (b) mean wave direction (deg) (c) and period (s) (d) recorded between July and December 2016 The surveys of 3 August and 18 November 2016 are shown by vertical grey bands

During the autumn of 2016 (September to December) more energetic events were observed Offshore significant wave heights reached a maximum of 38 m at G2 on 12 November but it happened at low tide and dissipation across the tidal flat prevented incoming waves to reach the beach On 16 November waves at the beachface toe reached 092 m and mean wave directions (observed offshore) spanned between 99deg and 123deg (ESE) throughout the day

23 LiDAR Survey and Topographic Data

A Mobile Terrestrial LiDAR System (MTLS) hereinafter referred to as LiDAR was used to acquire a high-resolution beach topography on 3 August 2016 (Figure 4) and 18 November 2016 Both surveys were conducted at low tide The LiDAR is mounted on a utility side by side vehicle (Figure 5b) moving at ~30ndash40 kmh The system includes a laser scanner Riegl VQ-250 and a GPS inertial navigation system Applanix POS-LV 220 Post-processing for raw data analysis is described in Didier et al [62] A manual filtering of the debris on the beach was made on the raw LiDAR data (dense point cloud) to delete trees and driftwood from the final LiDAR topography The RMS errors (x y z) (23 control points) between RTK-GPS (Trimble R8) and adjacent laser points was assessed in Didier et al [62] and are in the order plusmn003 m [62] The LiDAR data were used as a validation surface for each detected and orthorectified shoreline

Figure 3 Mean water level (with daily tidal cycles) (m) (a) significant wave height (m) (b) mean wavedirection () (c) and period (s) (d) recorded between July and December 2016 The surveys of 3 Augustand 18 November 2016 are shown by vertical grey bands

During the autumn of 2016 (September to December) more energetic events were observedOffshore significant wave heights reached a maximum of 38 m at G2 on 12 November but ithappened at low tide and dissipation across the tidal flat prevented incoming waves to reach the beachOn 16 November waves at the beachface toe reached 092 m and mean wave directions (observedoffshore) spanned between 99 and 123 (ESE) throughout the day


A Mobile Terrestrial LiDAR System (MTLS) hereinafter referred to as LiDAR was used to acquirea high-resolution beach topography on 3 August 2016 (Figure 4) and 18 November 2016 Both surveyswere conducted at low tide The LiDAR is mounted on a utility side by side vehicle (Figure 5b) movingat ~30ndash40 kmh The system includes a laser scanner Riegl VQ-250 and a GPS inertial navigationsystem Applanix POS-LV 220 Post-processing for raw data analysis is described in Didier et al [62]A manual filtering of the debris on the beach was made on the raw LiDAR data (dense point cloud)to delete trees and driftwood from the final LiDAR topography The RMS errors (x y z) (23 controlpoints) between RTK-GPS (Trimble R8) and adjacent laser points was assessed in Didier et al [62] andare in the order plusmn003 m [62] The LiDAR data were used as a validation surface for each detected andorthorectified shoreline


Figure 4 LiDAR point cloud from the survey of 3 August 2016 on the beach of Pointe-Lebel shown in aerial (a) and perspective (b) views

24 Video Monitoring Station

The beach of Pointe-Lebel was equipped with two AXIS P3367-VE video camera systems on 8 June 2016 (Figures 4b and 5) Both cameras are installed on a pole originally located at 5 m from the beach (49092degN 68227degW) The system is directly connected to a power grid Each camera covers a pan range of 84deg and a shadow zone varying from ~1 m near the system to ~10 m at the beachface toe is located between both camera views (no overlap between views) The total view reaches ~180deg Cameras are precisely located at 1208 m above the mean sea level and offer a continuous sampling acquisition of 4 Hz (2592 times 1944 pixels 5 megapixels) during daylight (~10 hday) Both Power Over the Ethernet (POE) IP cameras are accessible online for remote modifications via a 4G network and are connected on a laptop computer (Lenovo Intelreg Coretrade i5-6200 U CPU 23 GHzndash24 GHz 8 Go RAM 64 bits) located in a sealed box on the system post All videos are recorded directly on physical hard drives to assure and secure data acquisition during all weather conditions particularly during winter

Figure 4 LiDAR point cloud from the survey of 3 August 2016 on the beach of Pointe-Lebel shown inaerial (a) and perspective (b) views


The beach of Pointe-Lebel was equipped with two AXIS P3367-VE video camera systems on8 June 2016 (Figures 4b and 5) Both cameras are installed on a pole originally located at 5 m from thebeach (49092N 68227W) The system is directly connected to a power grid Each camera covers apan range of 84 and a shadow zone varying from ~1 m near the system to ~10 m at the beachfacetoe is located between both camera views (no overlap between views) The total view reaches ~180Cameras are precisely located at 1208 m above the mean sea level and offer a continuous samplingacquisition of 4 Hz (2592 times 1944 pixels 5 megapixels) during daylight (~10 hday) Both Power Overthe Ethernet (POE) IP cameras are accessible online for remote modifications via a 4G network and areconnected on a laptop computer (Lenovo Intelreg Coretrade i5-6200 U CPU 23 GHzndash24 GHz 8 Go RAM64 bits) located in a sealed box on the system post All videos are recorded directly on physical harddrives to assure and secure data acquisition during all weather conditions particularly during winter


Figure 5 The video camera system of Pointe-Lebel is located at 5 m from the beachface (a) Ground Control Points (GCPs showed as yellow dots) for image to ground coordinate conversions were acquired with RTKndashGPS on the beachface and the tidal flat during the LiDAR survey on the east (b) and west side of the beach (c) on 3 August 2016 The GCPs and LiDAR acquisitions are shown in (b)

25 Cameras Calibration

Optical measurements are subject to image distortions due to inherent camera characteristics The calibration process enables an estimation of these parameters and establishes a mapping conversion model from image to ground coordinates The technique described in Stumpf et al [63] was used because of its ability to deal with radial lens distortion and estimations for exterior orientation focal length skew factor and principal point The calibration requires at least nine ground control points (GCPs) for each camera that cover the entire camera view and that are evenly distributed on the beach and tidal flat at low tide Only GCPs closer than 230 m from the camera system (yellow dots in Figure 5bc east 33 GCPs west 38 GCPs) were used for the calibration in order to minimize the horizontal uncertainty resulting to final GCP RMSEs of 038 and 111 m for the west and east cameras respectively The mapping algorithm was then used to convert all detected shorelines (pixel coordinates) into ground coordinates

The alongshore pixel footprint increases with distance from the cameras Within 230 m projections of each pixel from camera dimensions to x-y geographical coordinates result in ground pixel footprints of 1 cm times 1 cm (longshore times cross-shore) close to the cameras It increases up to 28 m in the alongshore direction while the cross-shore footprint remains under 05 m

26 Shoreline Detection and Water Elevation Models

Five-minute video segments were acquired during the rising tide of 3 August 2016 between 1250 and 1600 (EST) and on 18 November 2016 between 1400 and 1730 (EST) LiDAR surveys were

Figure 5 The video camera system of Pointe-Lebel is located at 5 m from the beachface (a) GroundControl Points (GCPs showed as yellow dots) for image to ground coordinate conversions wereacquired with RTKndashGPS on the beachface and the tidal flat during the LiDAR survey on the east (b) andwest side of the beach (c) on 3 August 2016 The GCPs and LiDAR acquisitions are shown in (b)


Optical measurements are subject to image distortions due to inherent camera characteristicsThe calibration process enables an estimation of these parameters and establishes a mappingconversion model from image to ground coordinates The technique described in Stumpf et al [63] wasused because of its ability to deal with radial lens distortion and estimations for exterior orientationfocal length skew factor and principal point The calibration requires at least nine ground controlpoints (GCPs) for each camera that cover the entire camera view and that are evenly distributed on thebeach and tidal flat at low tide Only GCPs closer than 230 m from the camera system (yellow dotsin Figure 5bc east 33 GCPs west 38 GCPs) were used for the calibration in order to minimizethe horizontal uncertainty resulting to final GCP RMSEs of 038 and 111 m for the west and eastcameras respectively The mapping algorithm was then used to convert all detected shorelines(pixel coordinates) into ground coordinates

The alongshore pixel footprint increases with distance from the cameras Within 230 m projectionsof each pixel from camera dimensions to x-y geographical coordinates result in ground pixel footprintsof 1 cm times 1 cm (longshore times cross-shore) close to the cameras It increases up to 28 m in the alongshoredirection while the cross-shore footprint remains under 05 m



Five-minute video segments were acquired during the rising tide of 3 August 2016 between1250 and 1600 (EST) and on 18 November 2016 between 1400 and 1730 (EST) LiDAR surveys wereconducted simultaneously with video acquisitions All image analyses were performed using MatlabOutputs are 5-min time-averaged images (TIMEX) used for shoreline detections based on the presenceof swash at the waterbeach junction

The shoreline detection is applied in two steps [38] As a first approximation all shorelines wereautomatically extracted based on a blue green and red color ratio (B + GR) on all TIMEX imageswhere B G and R are respectively the pixel intensity values of the blue green and red channelsThe processing was computed for all pixels in a pre-defined region of interest (ROI) on the beach(Figure 6a) The shoreline represents a local minimum in B + GR ratios over the entire ROI with lowerratio values (pixel ratio values lt1) corresponding to the beachface while the water surface falls underhigher ratios typically over 1 The next step is the shoreline detection and identification on theTIMEX images The technique is based on the alongshore shape of the swash on the beach in theROI and detects the waterland boundary rather than drywet sand contours [38] Falsely detectedshorelines due to sunshine reflectance on the water debris at the shoreline and people on the beachwere discarded but no manual intervention was applied in the delineation


conducted simultaneously with video acquisitions All image analyses were performed using Matlab Outputs are 5-min time-averaged images (TIMEX) used for shoreline detections based on the presence of swash at the waterbeach junction

The shoreline detection is applied in two steps [38] As a first approximation all shorelines were automatically extracted based on a blue green and red color ratio (B + GR) on all TIMEX images where B G and R are respectively the pixel intensity values of the blue green and red channels The processing was computed for all pixels in a pre-defined region of interest (ROI) on the beach (Figure 6a) The shoreline represents a local minimum in B + GR ratios over the entire ROI with lower ratio values (pixel ratio values lt1) corresponding to the beachface while the water surface falls under higher ratios typically over 1 The next step is the shoreline detection and identification on the TIMEX images The technique is based on the alongshore shape of the swash on the beach in the ROI and detects the waterland boundary rather than drywet sand contours [38] Falsely detected shorelines due to sunshine reflectance on the water debris at the shoreline and people on the beach were discarded but no manual intervention was applied in the delineation

Figure 6 Time-averaged (TIMEX) image with a detected shoreline in calm conditions (red line) (a) and geo-rectified detected shoreline (red line) shown on a time-averaged image from the east camera of Pointe-Lebel (b)

Figure 6 Time-averaged (TIMEX) image with a detected shoreline in calm conditions (red line) (a) andgeo-rectified detected shoreline (red line) shown on a time-averaged image from the east camera ofPointe-Lebel (b)

Remote Sens 2017 9 826 10 of 22

In order to create video-derived digital elevation models (DEMs) from all the image-detectedshorelines a constant alongshore elevation value was first attributed to each detected shoreline [64]This step generally underlines some assumptions because knowing the exact shoreline elevation inthe absence of in-situ water levels and wave time series is not straightforward The association ofwater levels to shorelines is normally based on empirical formulations [65] Here two different waterelevation models (referred to M1 and M2) were tested and associated to the detected shorelines tocreate two different video-based DEMs Both water elevation models are associated to observedhydrodynamic parameters at G1 (at the beachface toe) The first model (M1) the image-detectedshoreline elevation based on the mean water level Zmwl is equal to ηtide which is the low-frequencystill water level (astronomical tide + barometric surge)

The second water elevation model (M2) represents the total water level (TWL) observed atthe beachwater line junction (Ztwl) It includes the still water level and the wave effect throughthe significant wave height All analyses were made during low energy conditions to minimizeuncertainties associated with high runupsetup values resulting from wave breaking [6566] Giventhat smaller waves are assumed to generate smaller runup values and therefore result in less detectionerrors due to swash length than during high energy conditions the approach was applied duringlow energy conditions only (Hs lt 015 m) M2 directly uses the nearshore observed significant waveheight instead of empirical runup equations based on offshore wave conditions that would obviouslyincrease the overall uncertainty of the approach (eg [6466]) The second model (M2) thus takes thefollowing form

Ztwl = ηtide + Hs (1)

The TWL model (M2) is justified by the fact that the Manicouagan peninsula mainly facessmall waves outside of storm events thus enabling the creation of DEMs with a high temporalresolution We validated this assumption by extracting the occurrence associated to Hs lt 015 mfrom a WAVEWATCH III (WW3) simulation at 83 km offshore eastward of the study site WW3 isforced with atmospheric forcing from the Climate Forecast System Reanalysis (CFSR NCEPNOAA)Oceanic forcing (currents water levels sea ice thickness and concentration) come from a RegionalOceanic Model (ROM) (5 km times 5 km grid) operated by the Institut des Sciences de la Mer de Rimouski(ISMER) Hourly waves were simulated for the 1980ndash2009 period at a 5-km grid Considering theentire 30 yrs time series offshore waves (Hs) are under 15 cm 58 of the time

Errors associated with the video-based approach have been assessed First the LiDAR elevationassociated to the detected shorelines was extracted to compare the overall effectiveness of both waterelevation models (Zmwl and Ztwl) This analysis enabled a direct comparison between the raw data(image-detected shorelines elevations) and LiDAR points prior to the creation of DEMs A TIN gridelevation was then created in LP360-QCoherent software from the LiDAR points and from bothmodels of video-based contour lines (M1 and M2) Point clouds were all sampled to a fixed squared20 cm times 20 cm mesh grid The latter was used to create the DEMs with the same pixel resolution of∆x = ∆y = 20 cm For both surveys the overall surface elevation (z) of the LiDAR-derived matriceswere compared to video-based DEMs with linear regression analysis

The shoreline detection xndashy errors were also analysed by extracting a reference contour line (rfl)associated to the mean high tide (z = 119) from all DEMs This elevation was chosen because it isprone to be reached on the beach virtually every day since it represents the mean level of high tidesThe cross-shore variability of the rfl was systematically calculated at 5 m alongshore intervals betweenvideo-derived models and LiDAR within the same date Furthermore the performance of the videosystem to estimate the temporal shoreline cross-shore net displacements was analyzed The cross-shoredisplacements of the rfl between November and August as observed with video cameras (∆yvid) werecompared to the displacements observed with the LiDAR system (∆ylid) Finally in order to analysethe potential of the video system to obtain morphological parameters on the beach in a context ofrisk management beachface slopes tan βbf particularly used in runup empirical equations [34] andnumerical models [67] were also calculated on these profiles using linear regression over the swash

Remote Sens 2017 9 826 11 of 22

zone In this paper tan βbf was calculated between the maximum water level on the beach and thebeachface toe

3 Results and Discussion

31 Shoreline Detection and Elevation Analyses

The video system provided continuous image acquisitions at 4 Hz during daylight on 3 Augustand 18 November 2016 on the beach of Pointe-Lebel The image analysis was performed on5-min TIMEX images during the rising tide while a pressure sensor located at the beach toesynchronously acquired mean water levels and wave characteristics This setting enabled the extractionof 21 (~83000 points) to 25 (~98000 points) contour lines during 3 August and 18 November 2016respectively This difference is solely due to a larger tidal amplitude on November 18

Most studies suggest using a longer averaging period usually 10 min TIMEX [4283639436568]to 15 min [69] The methodological choice of long period time-averaged images is based on thetime period of wave groups and the fact that a longer exposure enables a more complete ldquostoragerdquoof the processes on the beach such as wave breaking [21] that generate foam at the shoreline orover bars By recording all image intensities at each pixel during this period commonly useddetection techniques such as the SLIM (shoreline intensity maximum) ANN (artificial neural network)PIC (pixel intensity clustering) and CCD (color channel divergence) methods can be applied in mostcases [48] Using colour ratios combined with a pattern recognition algorithm does not necessarilyrequire such long TIMEX durations as noted by Almar et al [38] In fact using shorter time-averageddigital images (lt2 min) can overcome some errors that could result from the local swash length on thebeach Although the sensitivity of the detection from varying image-averaging time was not assessedin this paper 5 min-averaged shoreline detection was considered suitable for the rather steep beachfaceslope of Pointe-Lebel thus minimizing possible errors that could arise from the tidal variation over thecourse of longer time-averaging period [48]

An assessment of the video-derived shoreline vertical accuracies was made The correlationanalysis between detected shoreline points and their correspondence on the LiDAR DEM shows thatboth shoreline elevation models perform well (Figure 7) With vertical RMS errors of 006 m and highcorrelation coefficients (R2 = 099 p-value lt0001) (Table 1) both linear regressions are well correlated

Remote Sens 2017 9 826 11 of 22



The video system provided continuous image acquisitions at 4 Hz during daylight on 3 August and 18 November 2016 on the beach of Pointe-Lebel The image analysis was performed on 5-min TIMEX images during the rising tide while a pressure sensor located at the beach toe synchronously acquired mean water levels and wave characteristics This setting enabled the extraction of 21 (~83000 points) to 25 (~98000 points) contour lines during 3 August and 18 November 2016 respectively This difference is solely due to a larger tidal amplitude on November 18

Most studies suggest using a longer averaging period usually 10 min TIMEX [4283639436568] to 15 min [69] The methodological choice of long period time-averaged images is based on the time period of wave groups and the fact that a longer exposure enables a more complete ldquostoragerdquo of the processes on the beach such as wave breaking [21] that generate foam at the shoreline or over bars By recording all image intensities at each pixel during this period commonly used detection techniques such as the SLIM (shoreline intensity maximum) ANN (artificial neural network) PIC (pixel intensity clustering) and CCD (color channel divergence) methods can be applied in most cases [48] Using colour ratios combined with a pattern recognition algorithm does not necessarily require such long TIMEX durations as noted by Almar et al [38] In fact using shorter time-averaged digital images (lt2 min) can overcome some errors that could result from the local swash length on the beach Although the sensitivity of the detection from varying image-averaging time was not assessed in this paper 5 min-averaged shoreline detection was considered suitable for the rather steep beachface slope of Pointe-Lebel thus minimizing possible errors that could arise from the tidal variation over the course of longer time-averaging period [48]

An assessment of the video-derived shoreline vertical accuracies was made The correlation analysis between detected shoreline points and their correspondence on the LiDAR DEM shows that both shoreline elevation models perform well (Figure 7) With vertical RMS errors of 006 m and high correlation coefficients (R2 = 099 p-value lt0001) (Table 1) both linear regressions are well correlated

Figure 7 Video-based shoreline elevations versus LiDAR data as parameterized with the shoreline elevation model including only the mean water level (M1) (a) and the total water level (M2) (b) Regression lines are shown by red dashed lines The black line represents the 11 line

Differences lay mostly on the offset values with an origin offset of minus004 for the M1 (mean water level only) and minus002 m for M2 (total water level) respectively These results indicate that the overall contribution from Hs is small in the total water level approximation (M2) but it reduces the origin offset by 2 cm Slopes of both regression lines are very near 11 with the coefficient of the M2 being slightly higher than the M1 (103 instead of 100) Although the highest wave reached only 014 m this difference can be mainly explained by higher waves at high tide in the M2 model which is not considered in the mean water level model (M1) Almar et al [38] noted similar results indicating a positive and linear relationship between the detection errors and the local swash length which in

Figure 7 Video-based shoreline elevations versus LiDAR data as parameterized with the shorelineelevation model including only the mean water level (M1) (a) and the total water level (M2) (b)Regression lines are shown by red dashed lines The black line represents the 11 line

Differences lay mostly on the offset values with an origin offset of minus004 for the M1 (mean waterlevel only) and minus002 m for M2 (total water level) respectively These results indicate that the overallcontribution from Hs is small in the total water level approximation (M2) but it reduces the originoffset by 2 cm Slopes of both regression lines are very near 11 with the coefficient of the M2 being

Remote Sens 2017 9 826 12 of 22

slightly higher than the M1 (103 instead of 100) Although the highest wave reached only 014 mthis difference can be mainly explained by higher waves at high tide in the M2 model which is notconsidered in the mean water level model (M1) Almar et al [38] noted similar results indicating apositive and linear relationship between the detection errors and the local swash length which in returncould necessitate a correction factor to reduce uncertainties associated with runup excursions [48]This suggests that despite a lower origin offset in M2 using the mean water level only (M1) couldpotentially limit the uncertainty associated with higher wave heights at the beach Moreover the slopecoefficient of the regression line being precisely 100 in M1 suggests that using mean water levels onlyduring low wave conditions provides skillful results along the coast of Pointe-Lebel as also noted byMorris et al [45] in calm conditions on a tidal flat

32 Comparing Video- to LiDAR-Based Topography

Digital elevation models were created from video-detected shorelines and LiDAR cloud pointson the same 20 cm times 20 cm meshgrid for both surveys Both shoreline elevation models (from video)performed well in predicting the beachface shoreline elevations (Figure 7) and such correspondence isalso found on the resulting DEMs (results from August are shown as an example Figure 8) With theintegration of Hs and the mean water level M2 offers a more accurate morphology of the beachfacecompared to M1 The sediment accumulation on the west part of the beach clearly visible with theLiDAR survey is more perceived with M2 although it is still present on the DEM produced with M1Differences between video- and LiDAR-based topography were calculated to obtain a differentialDEM in the ROI (Figures 9 and 10) As shown in Figure 9ab the sediment accumulation on the westpart of the beach is explained by anthropogenic debris (hard plastic pipe drain) acting as an artificialtombolo The reference line of the mean high tides (z = 119 m) was extracted from the 3 Augustsurvey (from each raster) and is presented to give an overview of the erosion that occurred betweenthe surveys

Remote Sens 2017 9 826 12 of 22

return could necessitate a correction factor to reduce uncertainties associated with runup excursions[48] This suggests that despite a lower origin offset in M2 using the mean water level only (M1) could potentially limit the uncertainty associated with higher wave heights at the beach Moreover the slope coefficient of the regression line being precisely 100 in M1 suggests that using mean water levels only during low wave conditions provides skillful results along the coast of Pointe-Lebel as also noted by Morris et al [45] in calm conditions on a tidal flat


Digital elevation models were created from video-detected shorelines and LiDAR cloud points on the same 20 cm times 20 cm meshgrid for both surveys Both shoreline elevation models (from video) performed well in predicting the beachface shoreline elevations (Figure 7) and such correspondence is also found on the resulting DEMs (results from August are shown as an example Figure 8) With the integration of Hs and the mean water level M2 offers a more accurate morphology of the beachface compared to M1 The sediment accumulation on the west part of the beach clearly visible with the LiDAR survey is more perceived with M2 although it is still present on the DEM produced with M1 Differences between video- and LiDAR-based topography were calculated to obtain a differential DEM in the ROI (Figures 9 and 10) As shown in Figure 9ab the sediment accumulation on the west part of the beach is explained by anthropogenic debris (hard plastic pipe drain) acting as an artificial tombolo The reference line of the mean high tides (z = 119 m) was extracted from the 3 August survey (from each raster) and is presented to give an overview of the erosion that occurred between the surveys

Figure 8 Beachface topography of 3 August 2016 obtained from LiDAR (a) and video imagery as defined with (b) the shoreline elevation model 1 (M1 Zmwl) and (c) model 2 (M2 Ztwl)

Figure 8 Beachface topography of 3 August 2016 obtained from LiDAR (a) and video imagery asdefined with (b) the shoreline elevation model 1 (M1 Zmwl) and (c) model 2 (M2 Ztwl)


Figure 9 Overall differences between the LiDAR surveys and the video-based topography for 3 August (ab) and 18 November (cd) The 119 m rfl of 3 August as extracted from the LiDAR is shown on all figures

Figure 10 Relative frequency () of the Δ LiDAR-Zmwl (M1) and Δ LiDAR-Ztwl (M2) digital elevation modelsrsquo (DEMs) differences (over all pixels) as observed during 3 August (a) and 18 November (b) Positive values indicate higher elevations on the LiDAR and underestimations by the shoreline detection model

The sediment accumulation located on the west camera view (left side of Figure 9ab) creates a shadow zone preventing the west camera to precisely locate the shorelines This promotes rogue detections and therefore increased elevation uncertainties at this location On Figure 9cd this sediment accumulation is no longer in place due to the beachrsquos response to a storm event that occurred on 16 November As a result of sediment transport the video-derived topography at this location is well predicted in November (Figure 9cd)

The cameras in Pointe-Lebel being located at only 12 m above mean sea level all analyses are limited to 230 m from each camera to keep planimetric errors lower than ~1 m Within this distance despite the shadow zone mentioned above overall video-derived DEM elevation differences all range between minus025 m and 029 m Spatial coverage depends on the camera elevation [21] These errors could therefore be overcome by elevating the cameras as in previous works (eg McCarrol et al [70] (55 m) Bracs et al [65] (44 m) Seacuteneacutechal et al [32] Almar et al [38] (27 m) Vousdoukas et al [47] (20 m) Abessolo Ondoa et al [69] (15 m))

Figure 9 Overall differences between the LiDAR surveys and the video-based topography for 3 August(ab) and 18 November (cd) The 119 m rfl of 3 August as extracted from the LiDAR is shown onall figures

Remote Sens 2017 9 826 13 of 22





Figure 10 Relative frequency () of the ∆ LiDAR-Zmwl (M1) and ∆ LiDAR-Ztwl (M2) digital elevationmodelsrsquo (DEMs) differences (over all pixels) as observed during 3 August (a) and 18 November(b) Positive values indicate higher elevations on the LiDAR and underestimations by the shorelinedetection model

The sediment accumulation located on the west camera view (left side of Figure 9ab) createsa shadow zone preventing the west camera to precisely locate the shorelines This promotes roguedetections and therefore increased elevation uncertainties at this location On Figure 9cd this sedimentaccumulation is no longer in place due to the beachrsquos response to a storm event that occurred on16 November As a result of sediment transport the video-derived topography at this location is wellpredicted in November (Figure 9cd)

The cameras in Pointe-Lebel being located at only 12 m above mean sea level all analyses arelimited to 230 m from each camera to keep planimetric errors lower than ~1 m Within this distancedespite the shadow zone mentioned above overall video-derived DEM elevation differences all rangebetween minus025 m and 029 m Spatial coverage depends on the camera elevation [21] These errorscould therefore be overcome by elevating the cameras as in previous works (eg McCarrol et al [70]

Remote Sens 2017 9 826 14 of 22

(55 m) Bracs et al [65] (44 m) Seacuteneacutechal et al [32] Almar et al [38] (27 m) Vousdoukas et al [47](20 m) Abessolo Ondoa et al [69] (15 m))

According to the entire relative frequency of the elevation difference between the LiDAR andthe video-derived DEMs (Figure 10) the worst model for each survey date is systematically M1with a mean underestimation of 012 m (3 August) and 005 m (18 November) although M2 stillunderestimates the mean ground elevation by 004 m during 3 August The best elevation modelremains M2 especially on November 18 with a bias equal to zero and a standard deviation of 006 m(Figure 10b)

This range of uncertainty corresponds well to previous conclusions Vousdoukas et al [47]obtained vertical RMS errors of 026 m using empirical formulations based on tidal and wave runuplevels on the beach Similarly Uunk et al [36] mentioned elevation RMS errors between 028 and034 m the highest values being observed with a completely automated method These uncertaintiesmay in fact result from the underlying empirical relationships relating shoreline elevations to offshorewave characteristics and water levels [42] Instead of assuming empirical relationships to estimate theshoreline elevations the similar approach applied here uses in-situ water levels and only small waveheights (lt015 m) Similarly Blossier et al [71] selected only wave heights less than 1 m to minimizeerrors associated with wave setup estimations Errors obtained above are thus in the range of what wasobserved by Morris et al [45] whom by using DGPS-RTK for shoreline validations in a lagoon withoutwave effects obtained vertical accuracy in the order of 005 m The results presented here validate thevideo-derived elevation values with the LiDAR topographic surveys The LiDAR cloudpoint witha high precision (plusmn003 m) acts as a benchmark topography providing validation points virtuallyeverywhere on the beachface Using M2 to produce a beachface DEM gives an estimation of themorphology as skillful as the one acquired with the LiDAR survey

33 Cross-Shore Position of the Shoreline

To further investigate the performance of the video-based DEMs a cross-shore analysis ofshoreline migrations was made Such shoreline extraction technique is often used to establish long-termmorphodynamic patterns and has been proven to outperform common beach monitoring strategies(eg RTK-GPS profiles aerial LiDAR surveys) mostly because advantages provided by the low costsand the high spatiotemporal resolution overcome disadvantages of the limited coverage [10] Contraryto other techniques however the estimation of shoreline elevations and their cross-shore positionsystematically comes with some planimetric uncertainties that can reach many meters [66]

For each DEM the mean high tide reference line (119 m noted rfl) was extracted and smoothedusing a 5 m alongshore window to filter the smallest features on the beach such algae and wooddebris Its location between video- and LiDAR-based DEMs on a given date was first evaluated at65 cross-shore profiles with an alongshore spacing of 5 m As observed on Figure 11 cross-shorelocations of the rfl along the beach of Pointe-Lebel spanned between ~6 m and ~115 m from the landCross-shore locations of the rfl are well-correlated between LiDAR and video-derived beachface DEMsWith a correlation coefficient R2 = 095 (Table 1) and a root-mean-square error of 027 m M2 performsbetter than M1 (R2 = 081 RMSE = 052 m) Furthermore the mean deviation (MD) obtained with M2(031 m) is at least half the value of M1 (063 m)


Figure 11 Regression analysis of the cross-shore locations of the shoreline (rfl) between each video shoreline elevation model and LiDAR data (M1 blue M2 red plusmn confidence intervals (CI)) The black line represents the 11 line

Table 1 Regression analysis results and skills for the shoreline elevation models according to the evaluated morphological metrics (MD mean deviation MAD mean absolute deviation)

Analysis Model Fit Model Skill

R2 RMSE MD MAD

Shoreline detection (Δz) M1~LiDAR 100x minus 004 099 006 minus004 006 M2~LiDAR 103x minus 002 099 006 002 005

Cross-shore location (Δy) (same date) M1~LiDAR 083x + 090 081 052 063 066 M2~LiDAR 089x + 068 095 027 031 036

Cross-shore displacements (Δy) (multi-dates)

M1~LiDAR 111x + 092 097 035 083 085 M2~LiDAR 102x + 012 097 033 011 024

Beachface slopes (Δtan βbf) (August) M1~LiDAR 095x + 0006 073 0007 0014 M2~LiDAR 099x minus 0007 079 0006 0008

(November) M1~LiDAR 064x + 004 039 0005 0007 M2~LiDAR 102x 066 0004 0004

34 Spatiotemporal Analysis of the Morphological Evolution

The cross-shore shoreline displacement over time has also been assessed on the beach of Pointe-Lebel (Figure 12) Significant shoreline displacements were observed between 3 August and 18 November 2016 with the LiDAR surveys The net onshore shoreline displacement (with a maximum erosion of minus377 m) observed with LiDAR is located within the west camera view (Figure 12a) On the east view the LiDAR shows a rather different trend where a net displacement (minus278 m) close to the camera (y location 2517080895 m) is compensated by a net seaward displacement peaking at +130 m (y location 2518150266 m) Same trends are observed with both video-based models but the maximum erosionadvance observed at these same longshore locations are minus356 m+270 m and minus369 m+155 m with the M1 and M2 respectively

Figure 11 Regression analysis of the cross-shore locations of the shoreline (rfl) between each videoshoreline elevation model and LiDAR data (M1 blue M2 red plusmn confidence intervals (CI)) The blackline represents the 11 line

Table 1 Regression analysis results and skills for the shoreline elevation models according to theevaluated morphological metrics (MD mean deviation MAD mean absolute deviation)

Analysis Model FitModel Skill

R2 RMSE MD MAD

Shoreline detection (∆z)M1~LiDAR 100x minus 004 099 006 minus004 006M2~LiDAR 103x minus 002 099 006 002 005

Cross-shore location (∆y) (same date) M1~LiDAR 083x + 090 081 052 063 066M2~LiDAR 089x + 068 095 027 031 036

Cross-shore displacements (∆y)(multi-dates)

M1~LiDAR 111x + 092 097 035 083 085M2~LiDAR 102x + 012 097 033 011 024

Beachface slopes (∆tan βbf) (August) M1~LiDAR 095x + 0006 073 0007 0014M2~LiDAR 099x minus 0007 079 0006 0008

(November)M1~LiDAR 064x + 004 039 0005 0007M2~LiDAR 102x 066 0004 0004


The cross-shore shoreline displacement over time has also been assessed on the beach ofPointe-Lebel (Figure 12) Significant shoreline displacements were observed between 3 Augustand 18 November 2016 with the LiDAR surveys The net onshore shoreline displacement (with amaximum erosion of minus377 m) observed with LiDAR is located within the west camera view(Figure 12a) On the east view the LiDAR shows a rather different trend where a net displacement(minus278 m) close to the camera (y location 2517080895 m) is compensated by a net seawarddisplacement peaking at +130 m (y location 2518150266 m) Same trends are observed with bothvideo-based models but the maximum erosionadvance observed at these same longshore locationsare minus356 m+270 m and minus369 m+155 m with the M1 and M2 respectively


Figure 12 Longshore variation of the cross-shore position of the reference shoreline (119 m elevation contour) obtained during the surveys The patterns of erosionadvance observed with the LiDAR surveys (a) are also distinguished with both M1 (b) and M2 (c) but the net displacement (d) is best fitted by the M2 model (yellow curve panel d) Dots show the mean deviation (MD) of the data The pivotal point of the beach as detected with M2 is closely located to the smoothing window size (~525 m) (black circles panels andashc)

Both models skillfully predict shoreline displacement behaviors (R2 = 097) (Table 1) and the results are virtually the same M2 lowers to 033 m the planimetric RMSE compared to M1 (RMSE = 035 m) and the regression line is closer to 11 with a lower origin offset (102x + 012) As observed from the assessment of cross-shore positions (Figure 11) M2 indicates a net displacement that is closer (011 m) to the original displacement observed on LiDAR (compared to observations from M1) The latter indicates a quasi-systematically biased displacement of 083 m toward the water (Figure 12d green line) a result inherent to the mean water elevation association to the shoreline Integrating the RMS errors associated with the cross-shore location only (Section 34) total RMSE are still under 1 m for both M1 (087 m) and M2 (060 m) The shoreline elevation model M2 thus shows a closer relationship between the real displacements observed on the beach with the LiDAR and further agrees with the conclusions of Aarninkhof et al [42] stating that their detection technique (the Hue-Saturation-Value (HSV) method) identifies a shoreline at the higher end of the runup excursion As also noted above the results presented here using the Almarrsquos shoreline detection technique [38] justify the integration of waves as a proxy to runup into the shoreline elevation estimation

Figure 12 Longshore variation of the cross-shore position of the reference shoreline (119 m elevationcontour) obtained during the surveys The patterns of erosionadvance observed with the LiDARsurveys (a) are also distinguished with both M1 (b) and M2 (c) but the net displacement (d) is bestfitted by the M2 model (yellow curve panel d) Dots show the mean deviation (MD) of the dataThe pivotal point of the beach as detected with M2 is closely located to the smoothing window size(~525 m) (black circles panels andashc)

Both models skillfully predict shoreline displacement behaviors (R2 = 097) (Table 1) and theresults are virtually the same M2 lowers to 033 m the planimetric RMSE compared toM1 (RMSE = 035 m) and the regression line is closer to 11 with a lower origin offset (102x + 012)As observed from the assessment of cross-shore positions (Figure 11) M2 indicates a net displacementthat is closer (011 m) to the original displacement observed on LiDAR (compared to observationsfrom M1) The latter indicates a quasi-systematically biased displacement of 083 m toward thewater (Figure 12d green line) a result inherent to the mean water elevation association to theshoreline Integrating the RMS errors associated with the cross-shore location only (Section 34)total RMSE are still under 1 m for both M1 (087 m) and M2 (060 m) The shoreline elevationmodel M2 thus shows a closer relationship between the real displacements observed on the beachwith the LiDAR and further agrees with the conclusions of Aarninkhof et al [42] stating that theirdetection technique (the Hue-Saturation-Value (HSV) method) identifies a shoreline at the higher endof the runup excursion As also noted above the results presented here using the Almarrsquos shoreline

Remote Sens 2017 9 826 17 of 22

detection technique [38] justify the integration of waves as a proxy to runup into the shorelineelevation estimation

The comparison of the reference shorelinersquos alongshore position between the LiDAR surveysover time shows a rotation pattern on the beach of Pointe-Lebel (Figure 12) Such beach dynamic isthe result of both beach extremities showing contrasting behaviors erosion on one side and advanceon the other [71] This rotation could be related to the storms that occurred on 22 October andfrom 12 to 16 November the latter occurring two days before the second survey Although no wavedirections data in the nearshore zone could be related to the dynamic beach response the offshorewave directions were mainly from the south and ranged from 100 (22 October 16 November) to 230

(12 November) The pressure sensor G1 recorded wave heights reaching 098 m at the beach toe duringthe 279 m high tide on 16 November (1436 EST) (Figure 3) These events caused major erosion onthe beach

Previous studies have shown the efficiency of video monitoring stations to quantify beachrotations due to sediment transport processes [68] or beach exposure to wave directions [64] A pivotalpoint is indicated by a local minimum in the displacement of the shoreline [68] Indeed the highfrequency image acquisition enabled the observation of the beachrsquos pivotal rotation point both with M1and M2 shoreline elevation models (Figure 12bc) Extracted shorelines with M1 indicate the pivotalpointrsquos location at 1718 m westward of the LiDAR-derived point The distance is much improved withM2 being located at 525 m from the real central rotation point observed on the LiDAR This is in factwithin a close range of the longshore averaged shoreline 5-m window

Beachface slopes were also calculated from regression analysis over the 65 cross-shore profilesalong the beach from video- and LiDAR-based DEMs for both surveys (Figure 13) For the3 August survey beachface slopes obtained from LiDAR have scattered values showing a left-skeweddistribution ranging between 0082 and 0164 with an average of 0142 These values are typicallyassociated with steep slopes of reflective beachfaces [72] Slopes calculated from M1 and M2 arewell-correlated to the LiDAR-derived beach slopes (Table 1) with RMSE le 0007 for both models andR2 of 073 and 079 for M1 and M2 respectively However during the November survey regressiontrends are less defined Furthermore the average beachface slope is slightly lower in November (0124)with values ranging from 0116 to 0144 Indeed this is the result of net erosion and sediment transportthat occurred on November 16 on the upper beach The optimal model is M2 with RMSE = 0004and the correlation is strong (y = 102x R2 = 066) The regression is however not significant withM1 and beach slopes are thus not well correlated It appears to be the result of underestimationsof shoreline elevations with M1 near the high tide on 18 November giving smaller beachface slopevalues This observation is also noted on the August survey

Remote Sens 2017 9 826 17 of 22

The comparison of the reference shorelinersquos alongshore position between the LiDAR surveys over time shows a rotation pattern on the beach of Pointe-Lebel (Figure 12) Such beach dynamic is the result of both beach extremities showing contrasting behaviors erosion on one side and advance on the other [71] This rotation could be related to the storms that occurred on 22 October and from 12 to 16 November the latter occurring two days before the second survey Although no wave directions data in the nearshore zone could be related to the dynamic beach response the offshore wave directions were mainly from the south and ranged from 100deg (22 October 16 November) to 230deg (12 November) The pressure sensor G1 recorded wave heights reaching 098 m at the beach toe during the 279 m high tide on 16 November (1436 EST) (Figure 3) These events caused major erosion on the beach

Previous studies have shown the efficiency of video monitoring stations to quantify beach rotations due to sediment transport processes [68] or beach exposure to wave directions [64] A pivotal point is indicated by a local minimum in the displacement of the shoreline [68] Indeed the high frequency image acquisition enabled the observation of the beachrsquos pivotal rotation point both with M1 and M2 shoreline elevation models (Figure 12bc) Extracted shorelines with M1 indicate the pivotal pointrsquos location at 1718 m westward of the LiDAR-derived point The distance is much improved with M2 being located at 525 m from the real central rotation point observed on the LiDAR This is in fact within a close range of the longshore averaged shoreline 5-m window

Beachface slopes were also calculated from regression analysis over the 65 cross-shore profiles along the beach from video- and LiDAR-based DEMs for both surveys (Figure 13) For the 3 August survey beachface slopes obtained from LiDAR have scattered values showing a left-skewed distribution ranging between 0082 and 0164 with an average of 0142 These values are typically associated with steep slopes of reflective beachfaces [72] Slopes calculated from M1 and M2 are well-correlated to the LiDAR-derived beach slopes (Table 1) with RMSE le 0007 for both models and R2 of 073 and 079 for M1 and M2 respectively However during the November survey regression trends are less defined Furthermore the average beachface slope is slightly lower in November (0124) with values ranging from 0116 to 0144 Indeed this is the result of net erosion and sediment transport that occurred on November 16 on the upper beach The optimal model is M2 with RMSE = 0004 and the correlation is strong (y = 102x R2 = 066) The regression is however not significant with M1 and beach slopes are thus not well correlated It appears to be the result of underestimations of shoreline elevations with M1 near the high tide on 18 November giving smaller beachface slope values This observation is also noted on the August survey

Figure 13 Longshore variability in beachface slopes between video-based models and LiDAR-based DEMs The black line is the 11 line

35 Perpectives and Limitations

Compared to a LiDAR survey the video-derived DEM technique is less time-consuming and more cost-effective In low energy coastal environments such as the St Lawrence Estuary the technique makes it possible to exclude any uncertainties that are commonly induced by empirical

Figure 13 Longshore variability in beachface slopes between video-based models and LiDAR-basedDEMs The black line is the 11 line

Remote Sens 2017 9 826 18 of 22


Compared to a LiDAR survey the video-derived DEM technique is less time-consuming and morecost-effective In low energy coastal environments such as the St Lawrence Estuary the techniquemakes it possible to exclude any uncertainties that are commonly induced by empirical setuprunuprelationships It however implies some inherent centimeter-scale elevation uncertainties based on thesensorrsquos technical accuracy (manufacturer-specified accuracy plusmn5 mm of water depth) and mostly itsvertical positioning with RTK-GPS Moreover with the second model (M2) one needs to obtain Hs atthe beachface toe during low energy events Interestingly though on Pointe-Lebel beach it is possibleto apply this remote sensing technique 58 of the time (ie under such low conditions) includingduring winter when it is not always possible to access the beach with other monitoring strategiesbecause of snow and sea ice The possibility to acquire a beach topography with a high temporalfrequency could potentially highlight coastal processes occurring during winter season while land fastice is under constant modification in response to climate change Although there is high confidence ofa progressive disappearance of sea ice toward 2100 in the Gulf of the St Lawrence [56] more fielddata acquired with video stations on the dynamics of coastal sea ice in the St Lawrence Estuary couldprovide more knowledge on the impact of climate change in sub-arctic estuaries On more energeticcoasts higher values of Hs could increase the errors of this model (M2) and could prevent us fromapplying the method In such a case further sensitive analysis of the M2 results as a function of anincrease in Hs would need to be made

Contrary to LiDAR surveys the video-based technique is limited to the maximum extension ofthe tidal range during the survey (ie from the lowest to the highest elevation shorelines detected)Furthermore the automatic extraction technique applied here which undertakes a waterbeachdistinction from color band ratios and a shoreline shape recognition module [38] failed at low tidemainly because the water depth was too shallow therefore limiting swash detection and promotingnumerous rogue detections of the sandy bottom Vousdoukas et al [47] mentioned that rogue detectionscould be observed under high wave conditions therefore promoting some rogue automated detectionsErroneously identified shorelines in our study were rather the result of calm conditions during lowtide The technique applied on the steep beachface and dissipative tidal flat of Pointe-Lebel wouldtherefore need to be improved for shoreline detections at low tide or could be combined with a manualdetection to minimize the errors [66] while the water depth is especially low and the sandy bottom isvisible Such optical conditions prevent the discretization between the water and the beach

4 Conclusions

A shoreline detection technique from a low-cost video monitoring station was used to create digitalelevation models (DEMs) of a steep beachface on a tidal flat at two different dates Environmentalconditions were calm during both surveys with nearshore wave heights less than 15 cm Two shorelineelevation models were tested against LiDAR-derived shorelines and interpolated topography The firstmodel is the simplest and the elevation is only associated to the mean water level recorded at the beachtoe Although it performs relatively well to predict the shoreline position and elevation this modelgenerally underestimates individual shoreline elevations by roughly 4 cm and cross-shore positionserrors are in the order of 063 m (MD) toward the sea Although it is within an acceptable planimetricuncertainty (∆x ∆y lt 1 m) for spatiotemporal shoreline migration assessments a correction factorneeds to be applied to palliate this issue therefore the method is not necessarily an improvement overprevious studies The shoreline model including the effect of runup integrated here as a proxy viadirect measures of waves at the beachface toe performs better both in terms of overall vertical (∆z)and planimetric uncertainties (∆x ∆y) respectively lt6 cm and lt31 cm (MD) This approach greatlyfacilitates the acquisition of continuous coastal topographic observations that is virtually as effectiveas a LiDAR survey because it is not based on an estimation of wave-induced setup or runup thatwould imply some uncertainties in the shoreline location and elevation A digital elevation modelis usually necessary in geomorphological analysis in coastal studies and management This study

Remote Sens 2017 9 826 19 of 22

presented a video-based technique for the creation of DEMs that covers the range of video-detectedshorelines during a tidal cycle offering similar skills to a LiDAR-based raster surface with totalelevation errors under 11 cm Therefore the approach enables a precise measurement of morphologicalparameters used in geomorphology and coastal engineering (eg shoreline displacement beach slopeDEM sediment budget) while remaining an accessible approach for professionals responsible forcoastal risk management

Acknowledgments The authors thank the Queacutebec Ministry of Public Security for funding the project D Didierreceived a scholarship from the Fonds Queacutebeacutecois de la recherche sur la nature et les technologies The projectwould not have been possible without the research assistants from the LDGIZC (Canada) and LGO (France)The authors greatly acknowledge the coastal citizens of Pointe-Lebel for their support and constant surveillanceof the video station We would like to acknowledge five anonymous reviewers for their helpful suggestionsand comments

Author Contributions D Didier PB EA D Dumont and CD designed the research project D Didier and PBinstalled the video station EA CC D Didier and FF contributed to the image analysis tools D Didier EAEB LC processedanalyzed the data D Didier PB EA CC and D Dumont wrote the paper

Conflicts of Interest The authors declare no conflict of interest The founding sponsors had no role in the designof the study in the collection analyses or interpretation of data in the writing of the manuscript and in thedecision to publish the results

References

1 Masselink G Puleo JA Swash-zone morphodynamics Cont Shelf Res 2006 26 661ndash680 [CrossRef]2 Ruessink BG Kleinhans MG van den Beukel PGL Observations of swash under highly dissipative

conditions J Geophys Res Oceans 1998 103 3111ndash3118 [CrossRef]3 Sous D Petitjean L Bouchette F Rey V Meuleacute S Sabatier F Martins K Field evidence of swash

groundwater circulation in the microtidal rousty beach France Adv Water Resour 2016 97 144ndash155[CrossRef]

4 Huisman CE Bryan KR Coco G Ruessink BG The use of video imagery to analyse groundwater andshoreline dynamics on a dissipative beach Cont Shelf Res 2011 31 1728ndash1738 [CrossRef]

5 Vousdoukas MI Velegrakis AF Dimou K Zervakis V Conley DC Wave run-up observations inmicrotidal sediment-starved pocket beaches of the Eastern Mediterranean J Mar Syst 2009 78 S37ndashS47[CrossRef]

6 Yates ML Guza RT OrsquoReilly WC Equilibrium shoreline response Observations and modelingJ Geophys Res 2009 114 C09014 [CrossRef]

7 Hardisty J A morphodynamic model for beach gradients Earth Surf Process Landf 1986 11 327ndash333[CrossRef]

8 Aagaard T Black KP Greenwood B Cross-shore suspended sediment transport in the surf zoneA field-based parameterization Mar Geol 2002 185 283ndash302 [CrossRef]

9 Forbes DL Taylor RB Ice in the shore zone and the geomorphology of cold coasts Prog Phys Geogr 199418 59ndash89 [CrossRef]

10 Blossier B Bryan KR Daly CJ Winter C Spatial and temporal scales of shoreline morphodynamicsderived from video camera observations for the island of Sylt German Wadden Sea Geo-Mar Lett 2016[CrossRef]

11 Bernatchez P Fraser C Evolution of coastal defence structures and consequences for beach width trendsQueacutebec Canada J Coast Res 2012 285 1550ndash1566 [CrossRef]

12 Trenhaile AS Modeling the accumulation and dynamics of beaches on shore platforms Mar Geol 2004206 55ndash72 [CrossRef]

13 Didier D Bernatchez P Marie G Boucher-Brossard G Wave runup estimations on platform-beaches forcoastal flood hazard assessment Nat Hazards 2016 83 1143ndash1467 [CrossRef]

14 Flocrsquoh F Le Dantec N Lemos C Cancoueumlt R Sous D Petitjean L Bouchette F Ardhuin F Suanez SDelacourt C Morphological response of a macrotidal embayed beach Porsmilin France J Coast Res 201675 373ndash377 [CrossRef]

15 Tribbia J Moser SC More than information what coastal managers need to plan for climate changeEnviron Sci Policy 2008 11 315ndash328 [CrossRef]

Remote Sens 2017 9 826 20 of 22

16 List JH Farris AS Sullivan C Reversing storm hotspots on sandy beaches Spatial and temporalcharacteristics Mar Geol 2006 226 261ndash279 [CrossRef]

17 Cheng J Wang P Guo Q Measuring beach profiles along a low-wave energy microtidal coast West-CentralFlorida USA Geosciences 2016 6 44 [CrossRef]

18 Coco G Senechal N Rejas A Bryan KR Capo S Parisot JP Brown JA Macmahan JHM Beachresponse to a sequence of extreme storms Geomorphology 2014 204 493ndash501 [CrossRef]

19 Silveira TM Carapuccedilo AM Sousa H Taborda R Psuty NP Optimizing beach topographical fieldsurveys Matching the effort with the objectives J Coast Res 2013 1 588ndash593 [CrossRef]

20 Suanez S Cancoueumlt R Flocrsquoh F Blaise E Ardhuin F Filipot J-F Cariolet J-M Delacourt CObservations and predictions of wave runup extreme water levels and medium-term dune erosion duringstorm conditions J Mar Sci Eng 2015 3 674ndash698 [CrossRef]

21 McNinch JE Bar and swash imaging radar (BASIR) A mobile X-band radar designed for mapping nearshoresand bars and swash-defined shorelines over large distances J Coast Res 2007 231 59ndash74 [CrossRef]

22 Casella E Rovere A Pedroncini A Mucerino L Casella M Cusati LA Vacchi M Ferrari MFirpo M Study of wave runup using numerical models and low-altitude aerial photogrammetry A tool forcoastal management Estuar Coast Shelf Sci 2014 149 160ndash167 [CrossRef]

23 Mancini F Dubbini M Gattelli M Stecchi F Fabbri S Gabbianelli G Using unmanned aerial vehicles(uav) for high-resolution reconstruction of topography The structure from motion approach on coastalenvironments Remote Sens 2013 5 6880ndash6898 [CrossRef]

24 Holman RA Brodie KL Spore NJ Surf zone characterization using a small quadcopter Technical issuesand procedures IEEE Trans Geosci Remote Sens 2017 55 2017ndash2027 [CrossRef]

25 Vousdoukas MI Kirupakaramoorthy T Oumeraci H de la Torre M Wuumlbbold F Wagner BSchimmels S The role of combined laser scanning and video techniques in monitoring wave-by-wave swashzone processes Coast Eng 2014 83 150ndash165 [CrossRef]

26 Blenkinsopp CE Mole MA Turner IL Peirson WL Measurements of the time-varying free-surfaceprofile across the swash zone obtained using an industrial LIDAR Coast Eng 2010 57 1059ndash1065 [CrossRef]

27 Brodie KL Raubenheimer B Elgar S Slocum RK McNinch JE Lidar and pressure measurements ofinner-surfzone waves and setup J Atmos Ocean Technol 2015 32 1945ndash1959 [CrossRef]

28 Holman R Stanley J The history and technical capabilities of Argus Coast Eng 2007 54 477ndash491[CrossRef]

29 Pitman SJ Methods for field measurement and remote sensing of the swash zone In GeomorphologicalTechniques Cook SJ Clark LE Nield JM Eds British Society of Geomorphology Oxford UK 2014Volume 3 pp 1ndash14

30 Almar R Ibaceta R Blenkinsopp C Catalan P Cienfuegos R Trung Viet N Hai Thuan D VanUu D Lefebvre J-P Sowah Laryea W et al Swash-based wave energy reflection on natural beachesProc Coast Sediments 2015 1ndash13 [CrossRef]

31 Simarro G Guedes RMC Sancho A Guillen J Bryan KR Coco G On the use of variance images forrunup and shoreline detection Coast Eng 2015 99 136ndash147 [CrossRef]

32 Senechal N Coco G Bryan KR Holman RA Wave runup during extreme storm conditionsJ Geophys Res 2011 116 C07032 [CrossRef]

33 Stockdon HF Thompson DM Plant NG Long JW Evaluation of wave runup predictions fromnumerical and parametric models Coast Eng 2014 92 1ndash11 [CrossRef]

34 Stockdon H Holman R Howd P Sallenger A Empirical parameterization of setup swash and runupCoast Eng 2006 53 573ndash588 [CrossRef]

35 Vousdoukas MI Ferreira Oacute Almeida LP Pacheco A Toward reliable storm-hazard forecasts XBeachcalibration and its potential application in an operational early-warning system Ocean Dyn 2012 621001ndash1015 [CrossRef]

36 Uunk L Wijnberg KM Morelissen R Automated mapping of the intertidal beach bathymetry from videoimages Coast Eng 2010 57 461ndash469 [CrossRef]

37 Almar R Senechal N Coco G Estimation videacuteo haute freacutequence de la topographie inter- tidale drsquouneplage sableuse Application agrave la caracteacuterisation des seuils drsquoengraissement et drsquoeacuterosion In Xegravemes JourneacuteesNationales Geacutenie CocirctiermdashGeacutenie Civil Paralia Sophia Antipolis France 2008 pp 505ndash514

Remote Sens 2017 9 826 21 of 22

38 Almar R Ranasinghe R Seacuteneacutechal N Bonneton P Roelvink DJA Bryan KR Marieu V Parisot J-PSenechal N Bonneton P et al Video-based detection of shorelines at complex meso-macro tidal beachesJ Coast Res 2012 28 1040ndash1048 [CrossRef]

39 Pearre NS Puleo JA Quantifying seasonal shoreline variability at Rehoboth Beach Delaware usingautomated imaging techniques J Coast Res 2009 254 900ndash914 [CrossRef]

40 Holman R Sallenger A Lippmann T Haines J The application of video image processing to the study ofnearshore processes Oceanography 1993 6 78ndash85 [CrossRef]

41 Bernatchez P Dubois J-MM Seasonal quantification of coastal processes and cliff erosion on fine sedimentshorelines in a cold temperate climate north shore of the St Lawrence maritime estuary Queacutebec J Coast Res2008 1 169ndash180 [CrossRef]

42 Aarninkhof SGJ Turner IL Dronkers TDT Caljouw M Nipius L A video-based technique formapping intertidal beach bathymetry Coast Eng 2003 49 275ndash289 [CrossRef]

43 Plant NG Holman R A Intertidal beach profile estimation using video images Mar Geol 1997 140 1ndash24[CrossRef]

44 Madsen AJ Plant NG Intertidal beach slope predictions compared to field data Mar Geol 2001 173121ndash139 [CrossRef]

45 Morris BD Coco G Bryan KR Turner IL Street K Vale M Video-derived mapping of estuarineevolution J Coast Res 2007 2007 410ndash414

46 Turner IL Leyden VM System Description and Analysis of Shoreline Change August 1999ndashFebruary2000 Report 1 Northern Gold Coast Coastal Imaging System Water Research LaboratoryManly Vale NSW Australia 2000

47 Vousdoukas MI Ferreira PM Almeida LP Dodet G Psaros F Andriolo U Taborda R Silva ANRuano A Ferreira OacuteM Performance of intertidal topography video monitoring of a meso-tidal reflectivebeach in South Portugal Ocean Dyn 2011 61 1521ndash1540 [CrossRef]

48 Plant NG Aarninkhof SGJ Turner IL Kingston KS The performance of shoreline detection modelsapplied to video imagery J Coast Res 2007 23 658ndash670 [CrossRef]

49 Cariolet J-M Suanez S Runup estimations on a macrotidal sandy beach Coast Eng 2013 74 11ndash18[CrossRef]

50 Melby J Caraballo-Nadal N Kobayashi N Wave runup prediction for flood mapping In Proceedings ofthe Coastal Engineering Santander Spain 1ndash6 July 2012 Volume 1 p 79

51 Bernatchez P Dubois J-MM Bilan des connaissances de la dynamique de lrsquoeacuterosion des cocirctes du Queacutebecmaritime laurentien Geacuteographie Phys Quat 2004 58 45 [CrossRef]

52 Bernatchez P Eacutevolution Littorale Holocegravene et Actuelle des Complexes Deltaiumlques de Betsiamites et deManicouagan-Outardes Synthegravese Processus Causes et Perspectives PhD Thesis Universiteacute LavalVille de Queacutebec QC Canada 2003

53 Duchesne MJ Pinet N Beacutedard K St-Onge G Lajeunesse P Campbell DC Bolduc A Role of thebedrock topography in the Quaternary filling of a giant estuarine basin The Lower St Lawrence EstuaryEastern Canada Basin Res 2010 22 933ndash951 [CrossRef]

54 Pratte S Garneau M De Vleeschouwer F Late-Holocene atmospheric dust deposition in eastern Canada(St Lawrence North Shore) Holocene 2016 [CrossRef]

55 Senneville S St-Onge Drouin S Dumont D Bihan-Poudec M-C Belemaalem Z Corriveau MBernatchez P Beacutelanger S Tolszczuk-leclerc S Villeneuve R Modeacutelisation des Glaces dansLrsquoestuaire et le Golfe du Saint-Laurent dans la Perspective des Changements Climatiques Technical ReportUniversiteacute du Queacutebec agrave Rimouski Rimouski QC Canada 2014

56 Ruest B Neumeier U Dumont D Bismuth E Senneville S Caveen J Recent wave climate and expectedfuture changes in the seasonally ice-infested waters of the Gulf of St Lawrence Canada Clim Dyn 2015[CrossRef]

57 CHS Canadian Tides and Water Levels Data Archives Available online httpwwwisdm-gdsigccaisdm-gdsitwl-mneindex-enghtm (accessed on 20 March 2015)

58 Saucier FJ Chasseacute J Tidal circulation and buoyancy effects in the St Lawrence Estuary Atmos Ocean 200038 505ndash556 [CrossRef]

59 Masselink G Short AD The effect of tide range on beach morphodynamics and morphology A conceptualbeach model J Coast Res 1993 9 785ndash800

Remote Sens 2017 9 826 22 of 22

60 Scott T Masselink G Russell P Morphodynamic characteristics and classification of beaches in Englandand Wales Mar Geol 2011 286 1ndash20 [CrossRef]

61 Dupuis L Ouellet Y Preacutevision des vagues dans lrsquoestuaire du Saint-Laurent agrave lrsquoaide drsquoun modegravelebidimensionnel Can J Civ Eng 1999 26 713ndash723 [CrossRef]

62 Didier D Bernatchez P Boucher-Brossard G Lambert A Fraser C Barnett R Van-Wierts S Coastalflood assessment based on field debris measurements and wave runup empirical model J Mar Sci Eng2015 3 560ndash590 [CrossRef]

63 Stumpf A Augereau E Delacourt C Bonnier J Photogrammetric discharge monitoring of small tropicalmountain rivers A case study at Riviegravere des Pluies Reacuteunion Island Water Resour Res 2016 52 4550ndash4570[CrossRef]

64 Harley MD Turner IL Short AD Ranasinghe R Assessment and integration of conventional RTK-GPSand image-derived beach survey methods for daily to decadal coastal monitoring Coast Eng 2011 58194ndash205 [CrossRef]

65 Bracs MA Turner IL Splinter KD Short AD Lane C Davidson MA Goodwin ID Pritchard TCameron D Evaluation of opportunistic shoreline monitoring capability utilizing existing ldquosurfcamrdquoinfrastructure J Coast Res 2016 319 542ndash554 [CrossRef]

66 Angnuureng DB Almar R Senechal N Castelle B Addo KA Marieu V Ranasinghe R Shorelineresilience to individual storms and storm clusters on a meso-macrotidal barred beach Geomorphology 2017290 265ndash276 [CrossRef]

67 Poate TG McCall RT Masselink G A new parameterisation for runup on gravel beaches Coast Eng2016 117 176ndash190 [CrossRef]

68 Ojeda E Guilleacuten J Shoreline dynamics and beach rotation of artificial embayed beaches Mar Geol 2008253 51ndash62 [CrossRef]

69 Abessolo Ondoa G Almar R Kestenare E Bahini A Houngue G-H Jouanno J Du Penhoat YCastelle B Melet A Meyssignac B et al Potential of video cameras in assessing event and seasonalcoastline behaviour Grand Popo Benin (Gulf of Guinea) J Coast Res 2016 75 442ndash446 [CrossRef]

70 McCarroll RJ Brander RW Turner IL Leeuwen B Van Shoreface storm morphodynamics and mega-ripevolution at an embayed beach Bondi Beach NSW Australia Cont Shelf Res 2016 116 74ndash88 [CrossRef]

71 Blossier B Bryan KR Daly CJ Winter C Nearshore sandbar rotation at single-barred embayed beachesJ Geophys Res Oceans 2016 121 2286ndash2313 [CrossRef]

72 Wright LD Short AD Morphodynamic variability of surf zones and beaches A synthesis Mar Geol1984 56 93ndash118 [CrossRef]

copy 2017 by the authors Licensee MDPI Basel Switzerland This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (httpcreativecommonsorglicensesby40)

Introduction

Materials and Methods

Study Area

Wave and Tidal Data

LiDAR Survey and Topographic Data

Video Monitoring Station

Cameras Calibration

Shoreline Detection and Water Elevation Models

Results and Discussion

Shoreline Detection and Elevation Analyses

Comparing Video- to LiDAR-Based Topography

Cross-Shore Position of the Shoreline

Spatiotemporal Analysis of the Morphological Evolution

Perpectives and Limitations

Conclusions


Multi-year analyses of the shoreline position can show variabilities on a kilometer scale on sandycoasts [1011] while geologically constrained and protected systems like platform-beaches [1213] andpocket-beaches [14] can show little changes Capturing relevant morphogenic processes across differentscales is not straightforward namely because of the low temporal resolution of most monitoringstrategies (large time interval between field campaigns) [10] that filter short-term events Alongwith climate change and sea-level rise beach morphodynamics can therefore be confusing andmisunderstood by coastal managers and engineers in need of operational tools in order to dealwith constantly evolving beaches [15] On erosion hotspots [16] it is imperative to better understandthe multi-scale variability of the beach morphology and slope which are essential for flood riskassessment and to design protective structures in suitable adaptation strategies

Out of many technological tools for beach morphology monitoring such as total stations [17]DGPS surveys [18ndash20] radar [21] UAV [22ndash24] or LiDAR [25ndash27] perhaps the shore-based videoimagery is the most versatile Such systems can be installed virtually everywhere for continuousrecording over a long period of time [28] and are not directly vulnerable to extreme events as opposedto in-situ instruments [29] In terms of spatiotemporal resolutions video cameras offer other greatperspectives enabling both short-term and long-term monitoring capabilities Video imagery is clearlyeffective for wave runup measurements including swash and setup [30ndash33] It then can be appliedto calibrate empirical formulae in low and high energy situations through time-stack analysis [3435]Applied on video imagery shoreline detection techniques based on time-exposure images can alsoacquire high-frequency topographical information when combined with hydrodynamic forcing [36ndash38]With a relatively low cost [39] one can obtain digital elevation models (DEMs) with a few centimetresaccuracy [37] However such systems and subsequent image analysis needs to be adapted to localconditions since relationships between image optical characteristics and environmentalgeometricalconditions on beaches are not always understood [40] especially in cold environments where coastalsea ice interferes with sediment dynamics [41]

A video-based shoreline evolution monitoring typically integrates two parameters the shorelinedetection and the water elevation model [42] Swash and wave breaking on reflective beaches typicallyenhance beachwater pixel gradient intensities seen as foamy bright bands facilitating its detectionusing grey-scale images [4344] However detecting shorelines on dissipative beaches with large surfzones is not as straightforward since the cross-shore pixel intensitiesvariance due to waves andfoam motions [38] is scattered along a larger area Another problem arises in enclosed areas withless energetic conditions such as estuaries [45] or on the landward side of sandbars [37] To avoidthese problems some authors have proposed shoreline detection techniques based on full colorcharacteristics of the imagery especially using ARGUS stations [4246] as opposed to traditionalgrey-scale image analysis developed earlier [43] Almar et al [37] also detected shorelines basedon color analysis but added a correction due to alongshore swash shapes at different tidal levels toestimate the intertidal topography at a double-barred and energetic meso-tidal beach in BiscarroseFrance The resulting morphology performed well with GPS validation runs for low energeticconditions (significant wave height Hs lt 1 m) Detected planimetric shorelines can be compared toGPS runs on the beach at the time of the detection with horizontal uncertainties generally varyingbetween ~1 and 6 m [424547]

The water elevation associated with a detected shoreline commonly includes the still waterlevel (astronomical tide plus the barometric surge) and wave-induced runup (including setup andswash) [10374748] The former component the offshore static water level is mostly known within-situ gauge prior wave breaking The latter is more complicated to obtain since foreshore dynamicsare most notably deduced with empirical formulations Aarninkhof et al [42] computed the setupand swash oscillation elevations and concluded that their technique detected a shoreline at the upperend of the swash runup Apart from uncertainties in planimetric shoreline detections and inherentto RTK-GPS validations and ground control points these techniques imply uncertainties associatedwith the empirical formulations used Such equations are limited to the input conditions only and





21 Study Area

























































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R2 RMSE MD MAD




M1~LiDAR 111x + 092 097 035 083 085 M2~LiDAR 102x + 012 097 033 011 024








R2 RMSE MD MAD




M1~LiDAR 111x + 092 097 035 083 085M2~LiDAR 102x + 012 097 033 011 024










Remote Sens 2017 9 826 17 of 22






Remote Sens 2017 9 826 17 of 22








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4 Conclusions


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References
















Remote Sens 2017 9 826 20 of 22























Remote Sens 2017 9 826 21 of 22























Remote Sens 2017 9 826 22 of 22















Introduction


Study Area

Wave and Tidal Data



Cameras Calibration








Conclusions





21 Study Area

























































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Remote Sens 2017 9 826 11 of 22







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Remote Sens 2017 9 826 12 of 22




Remote Sens 2017 9 826 12 of 22












Remote Sens 2017 9 826 13 of 22








Remote Sens 2017 9 826 14 of 22











R2 RMSE MD MAD




M1~LiDAR 111x + 092 097 035 083 085 M2~LiDAR 102x + 012 097 033 011 024








R2 RMSE MD MAD




M1~LiDAR 111x + 092 097 035 083 085M2~LiDAR 102x + 012 097 033 011 024










Remote Sens 2017 9 826 17 of 22






Remote Sens 2017 9 826 17 of 22








Remote Sens 2017 9 826 18 of 22




4 Conclusions


Remote Sens 2017 9 826 19 of 22





References
















Remote Sens 2017 9 826 20 of 22























Remote Sens 2017 9 826 21 of 22























Remote Sens 2017 9 826 22 of 22















Introduction


Study Area

Wave and Tidal Data



Cameras Calibration








Conclusions






















































Remote Sens 2017 9 826 10 of 22







Remote Sens 2017 9 826 11 of 22







Remote Sens 2017 9 826 11 of 22










Remote Sens 2017 9 826 12 of 22




Remote Sens 2017 9 826 12 of 22












Remote Sens 2017 9 826 13 of 22








Remote Sens 2017 9 826 14 of 22











R2 RMSE MD MAD




M1~LiDAR 111x + 092 097 035 083 085 M2~LiDAR 102x + 012 097 033 011 024








R2 RMSE MD MAD




M1~LiDAR 111x + 092 097 035 083 085M2~LiDAR 102x + 012 097 033 011 024










Remote Sens 2017 9 826 17 of 22






Remote Sens 2017 9 826 17 of 22








Remote Sens 2017 9 826 18 of 22




4 Conclusions


Remote Sens 2017 9 826 19 of 22





References
















Remote Sens 2017 9 826 20 of 22























Remote Sens 2017 9 826 21 of 22























Remote Sens 2017 9 826 22 of 22















Introduction


Study Area

Wave and Tidal Data



Cameras Calibration








Conclusions

Remote Sens 2017 9 826 11 of 22







Remote Sens 2017 9 826 11 of 22










Remote Sens 2017 9 826 12 of 22




Remote Sens 2017 9 826 12 of 22












Remote Sens 2017 9 826 13 of 22








Remote Sens 2017 9 826 14 of 22











R2 RMSE MD MAD




M1~LiDAR 111x + 092 097 035 083 085 M2~LiDAR 102x + 012 097 033 011 024








R2 RMSE MD MAD




M1~LiDAR 111x + 092 097 035 083 085M2~LiDAR 102x + 012 097 033 011 024










Remote Sens 2017 9 826 17 of 22






Remote Sens 2017 9 826 17 of 22








Remote Sens 2017 9 826 18 of 22




4 Conclusions


Remote Sens 2017 9 826 19 of 22





References
















Remote Sens 2017 9 826 20 of 22























Remote Sens 2017 9 826 21 of 22























Remote Sens 2017 9 826 22 of 22















Introduction


Study Area

Wave and Tidal Data



Cameras Calibration








Conclusions

Remote Sens 2017 9 826 12 of 22




Remote Sens 2017 9 826 12 of 22












Remote Sens 2017 9 826 13 of 22








Remote Sens 2017 9 826 14 of 22











R2 RMSE MD MAD




M1~LiDAR 111x + 092 097 035 083 085 M2~LiDAR 102x + 012 097 033 011 024








R2 RMSE MD MAD




M1~LiDAR 111x + 092 097 035 083 085M2~LiDAR 102x + 012 097 033 011 024










Remote Sens 2017 9 826 17 of 22






Remote Sens 2017 9 826 17 of 22








Remote Sens 2017 9 826 18 of 22




4 Conclusions


Remote Sens 2017 9 826 19 of 22





References
















Remote Sens 2017 9 826 20 of 22























Remote Sens 2017 9 826 21 of 22























Remote Sens 2017 9 826 22 of 22















Introduction


Study Area

Wave and Tidal Data



Cameras Calibration








Conclusions







Remote Sens 2017 9 826 13 of 22








Remote Sens 2017 9 826 14 of 22











R2 RMSE MD MAD




M1~LiDAR 111x + 092 097 035 083 085 M2~LiDAR 102x + 012 097 033 011 024








R2 RMSE MD MAD




M1~LiDAR 111x + 092 097 035 083 085M2~LiDAR 102x + 012 097 033 011 024










Remote Sens 2017 9 826 17 of 22






Remote Sens 2017 9 826 17 of 22








Remote Sens 2017 9 826 18 of 22




4 Conclusions


Remote Sens 2017 9 826 19 of 22





References
















Remote Sens 2017 9 826 20 of 22























Remote Sens 2017 9 826 21 of 22























Remote Sens 2017 9 826 22 of 22















Introduction


Study Area

Wave and Tidal Data



Cameras Calibration








Conclusions

Remote Sens 2017 9 826 14 of 22











R2 RMSE MD MAD




M1~LiDAR 111x + 092 097 035 083 085 M2~LiDAR 102x + 012 097 033 011 024








R2 RMSE MD MAD




M1~LiDAR 111x + 092 097 035 083 085M2~LiDAR 102x + 012 097 033 011 024










Remote Sens 2017 9 826 17 of 22






Remote Sens 2017 9 826 17 of 22








Remote Sens 2017 9 826 18 of 22




4 Conclusions


Remote Sens 2017 9 826 19 of 22





References
















Remote Sens 2017 9 826 20 of 22























Remote Sens 2017 9 826 21 of 22























Remote Sens 2017 9 826 22 of 22















Introduction


Study Area

Wave and Tidal Data



Cameras Calibration








Conclusions





R2 RMSE MD MAD




M1~LiDAR 111x + 092 097 035 083 085 M2~LiDAR 102x + 012 097 033 011 024








R2 RMSE MD MAD




M1~LiDAR 111x + 092 097 035 083 085M2~LiDAR 102x + 012 097 033 011 024










Remote Sens 2017 9 826 17 of 22






Remote Sens 2017 9 826 17 of 22








Remote Sens 2017 9 826 18 of 22




4 Conclusions


Remote Sens 2017 9 826 19 of 22





References
















Remote Sens 2017 9 826 20 of 22























Remote Sens 2017 9 826 21 of 22























Remote Sens 2017 9 826 22 of 22















Introduction


Study Area

Wave and Tidal Data



Cameras Calibration








Conclusions






Remote Sens 2017 9 826 17 of 22






Remote Sens 2017 9 826 17 of 22








Remote Sens 2017 9 826 18 of 22




4 Conclusions


Remote Sens 2017 9 826 19 of 22





References
















Remote Sens 2017 9 826 20 of 22























Remote Sens 2017 9 826 21 of 22























Remote Sens 2017 9 826 22 of 22















Introduction


Study Area

Wave and Tidal Data



Cameras Calibration








Conclusions

Remote Sens 2017 9 826 18 of 22




4 Conclusions


Remote Sens 2017 9 826 19 of 22





References
















Remote Sens 2017 9 826 20 of 22























Remote Sens 2017 9 826 21 of 22























Remote Sens 2017 9 826 22 of 22















Introduction


Study Area

Wave and Tidal Data



Cameras Calibration








Conclusions

Remote Sens 2017 9 826 19 of 22





References
















Remote Sens 2017 9 826 20 of 22























Remote Sens 2017 9 826 21 of 22























Remote Sens 2017 9 826 22 of 22















Introduction


Study Area

Wave and Tidal Data



Cameras Calibration








Conclusions

Remote Sens 2017 9 826 20 of 22























Remote Sens 2017 9 826 21 of 22























Remote Sens 2017 9 826 22 of 22















Introduction


Study Area

Wave and Tidal Data



Cameras Calibration








Conclusions

Remote Sens 2017 9 826 21 of 22























Remote Sens 2017 9 826 22 of 22















Introduction


Study Area

Wave and Tidal Data



Cameras Calibration








Conclusions

Remote Sens 2017 9 826 22 of 22















Introduction


Study Area

Wave and Tidal Data



Cameras Calibration








Conclusions

LiDAR Validation of a Video-Derived Beachface Topography ... · from 0.5 to 2 m [13,49,50]. In...

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Transcript of LiDAR Validation of a Video-Derived Beachface Topography ... · from 0.5 to 2 m [13,49,50]. In...